| Term | Description |
|---|---|
| Convolution | Applying some filter on an image so certain features in the image get emphasized |
We did element wise product then we get the sum of the result matrix; so:
3*1 + 1*0 + 1*(-1)
+
1*1 + 0*0 + 7*(-1)
+
2*1 + 3*0 + 5*(-1)
=
-7
And so on for other elements 🙃
An application of convolution operation
Result: horizontal lines pop out
Result: vertical lines pop out
There are a lot of ways we can put number inside elements of the filter.
For example Sobel filter is like:
1 0 -1
2 0 -2
1 0 -1
Scharr filter is like:
3 0 -3
10 0 -10
3 0 -3
Prewitt filter is like:
-1 0 1
-1 0 1
-1 0 1
So the point here is to pay attention to the middle row
And Roberts filter is like:
1 0
0 -1
We can tune these numbers by ML approach; we can say that the filter is a group of weights that:
w1 w2 w3
w4 w5 w6
w7 w8 w9
By that we can get -learned- horizontal, vertical, angled, or any edge type automatically rather than getting them by hand.
If we have an n*n image and we convolve it by f*f filter the the output image will be n-f+1*n-f+1
- 🌀 If we apply many filters then our image shrinks.
- 🤨 Pixels at corners aren't being touched enough, so we are throwing away a lot of information from the edges of the image .
We can pad the image 💪